HSE National Waiting List Dashboard

County Risk Treemap · Long-wait ratio

Executive insight from the analysis

At a national level, the waiting list remains under sustained pressure, with approximately 730,000 patients waiting as of October 2025. While the proportion of long-waiters (≥12 months) has fallen meaningfully since early 2023, the absolute number of patients waiting over a year remains high at more than 126,000.

This creates a mixed picture. The system has become better at preventing waits from worsening, but it has not yet created enough sustained capacity to reduce the overall backlog. The data suggests structural pressure rather than short-term volatility.

OP and IPDC pathway insight

Analysis of OP and IPDC pathways shows distinct dynamics. Outpatient (OP) waiting lists account for the majority of total volume and drive most of the month-to-month volatility, particularly in high-demand medical specialties. In contrast, IPDC waiting lists are smaller in volume but contribute disproportionately to long-wait severity.

This indicates that OP growth reflects demand pressure, while IPDC backlogs reflect constrained downstream capacity. Treating both pathways with the same intervention risks addressing symptoms rather than causes.

What the data suggests should change

• Shift success measures away from total waiting numbers and towards ≥12-month cohorts. The long-wait ratio and severity index trends show that focusing on headline volumes alone can mask persistent structural delay.
• Concentrate effort on a small number of high-impact specialties. The Top-10 specialty analysis shows that Dermatology and Orthopaedics account for a disproportionate share of long waits, meaning marginal capacity added elsewhere will have limited system-wide effect.
• Differentiate OP and IPDC interventions. OP backlogs benefit most from demand smoothing and access expansion, while IPDC backlogs require sustained theatre, bed, and staffing capacity to reduce long waits meaningfully.

Practical actions informed by the dashboard

• Ring-fence additional capacity for patients waiting 12–18 months and 18+ months, rather than distributing activity evenly across all waiting bands.
• Use hospital-level concentration analysis to target intervention. The Top-10 hospital and Pareto charts show that a small number of hospitals (e.g. [hospital name]) account for the majority of long-waiters over the selected period.
• Use county-level variation to guide regional action. Counties such as Galway and Cork consistently exhibit higher long-wait ratios, suggesting that regional rebalancing may deliver more impact than national blanket measures.

Data source and preparation

• The data was sourced from publicly available National Treatment Purchase Fund (NTPF) waiting list publications, which aggregate and publish national outpatient and inpatient/day case waiting list data reported by HSE hospitals.
• Separate annual datasets for OP and IPDC were collected and consolidated into a single longitudinal dataset to enable time-based analysis.
• Data was merged at both specialty level and hospital level to support national, regional, and service-specific insights.
• Additional contextual fields, including county and province, were integrated to enable geographic deep-dives and regional comparison of waiting list pressure.
• Prior to analysis, the data was cleaned and standardised. This included harmonising date formats, aligning specialty and hospital naming conventions, validating totals against waiting-time bands, and removing incomplete records.
• No personal or identifiable patient data was used. All data is aggregated and anonymised in line with GDPR requirements.

Data governance, quality and provenance

The analysis is based on HSE administrative waiting list snapshot data from January 2023 to October 2025. A total of 9,418 records were analysed after preprocessing, with no missing values detected in critical analytical fields.

All transformations and metrics are reproducible. For operational deployment, data lineage, version control, and ownership should be formally documented to ensure transparency and auditability.

Limitations and risks

Forecasts are trend-based and assume continuity of recent patterns. They do not capture sudden policy changes, workforce shocks, or one-off initiatives. The severity index highlights system pressure and should not be interpreted as clinical risk.

Next steps

• Set explicit reduction targets for ≥12-month waits in the highest-strain specialties and track progress monthly using this dashboard.
• Extend forecasting to specialty- and hospital-level scenarios to support targeted, evidence-based capacity planning rather than national averages alone.

Technical Reflection

AI tools were used throughout the project as a brainstorming and editorial partner rather than as an automated solution. AI supported the exploration of dashboard structure, suggested analytical approaches, and helped draft code snippets and explanatory text, which were then reviewed, adapted, and integrated into the final solution. Working with AI also highlighted the importance of precise prompting. Providing clear context, example outputs, and iterative feedback significantly improved the quality and usefulness of responses, whereas vague prompts often required multiple revisions. Several challenges required manual problem-solving beyond AI assistance. These included embedding a large volume of cleaned data as JavaScript objects, converting the HSE logo into base64 format to meet the single-file requirement and debugging individual sections of the codebase as the dashboard grew in size and complexity. As the file expanded, careful step-by-step testing was needed to ensure that changes in one section did not unintentionally affect others. AI was most effective in accelerating development and refining presentation, while core design decisions, data preparation, validation of results, and interpretation of insights were handled manually. This combination allowed the project to remain analytically sound, reproducible, and aligned with the business intelligence objectives which may solve help real world issues.